
from termcolor import colored
from sklearn.tree import DecisionTreeClassifier
import missingno as msno
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn import metrics
import pickle
import pprint
from sklearn.ensemble import RandomForestRegressor
from pandas_profiling import ProfileReport
from dateutil import relativedelta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
from sklearn.model_selection import train_test_split
from statsmodels.regression.linear_model import OLS

plot_______ = False
plot_______ = True

def new_line():
    print("\n-------------------------\n")

def RMSE(predictions):
    return round(np.sqrt(((test_y - predictions)**2).mean()))

def plot_numerical_columns(col_name):
    if not plot_______:
        return None

    # Histogram
    df[col_name].plot(kind="hist", figsize=(13,8));
    plt.title(col_name, size=18);
    plt.axhline(y=df[col_name].mean(), color='red');
    plt.axhline(y=df[col_name].median(), color='green');
    plt.legend(['Actual', 'Mean', 'Median']);
    plt.show()

    # Scatter plot
    df[col_name].plot(figsize=(13,8));
    plt.title(col_name, size=18);
    plt.axhline(y=df[col_name].mean(), color='red');
    plt.axhline(y=df[col_name].median(), color='green');
    plt.legend(['Actual', 'Mean', 'Median']);
    plt.show()

    # scatter plot (sort by values), values Vs index
    df[col_name].sort_values().reset_index(drop=True).plot(figsize=(13,8));
    plt.title(col_name+" (SORTED)", size=18);
    plt.axhline(y=df[col_name].mean(), color='red');
    plt.axhline(y=df[col_name].median(), color='green');
    plt.legend(['Actual', 'Mean', 'Median']);
    plt.show()

    # box plot
    df[col_name].plot(kind="box", figsize=(13,8))
    plt.title(col_name, size=18);
    plt.xlabel("");
    plt.show()

def plot_date_columns(col_name):
    if not plot_______:
        return None
    df[col_name].plot(figsize=(15,7), grid=True);
    plt.xlabel("Index", size=14);
    plt.ylabel("Date", size=14);
    plt.title(col_name + " Graph", size=18);
    plt.show();

    df[col_name].sort_values().reset_index(drop=True).plot(figsize=(15,7), grid=True);
    plt.xlabel("Index (sorted)", size=14);
    plt.ylabel("Year", size=14);
    plt.title(col_name + " Graph", size=18);
    plt.show();

    (df[col_name].dt.year.value_counts(sort=False).sort_index() / len(df) * 100).plot(kind="bar", figsize=(15,7), grid=True);
    plt.xlabel("Year", size=14);
    plt.ylabel("Ratio (1-100)", size=14);
    plt.title(col_name + " year Frequency Graph", size=18);
    plt.show();

    (df[col_name].dt.month.value_counts().sort_index()/len(df) * 100).plot(kind="bar", figsize=(15,7), grid=True);
    plt.xlabel("Month", size=14);
    plt.ylabel("Ratio (1-100)", size=14);
    plt.title(col_name + " month Frequency Graph", size=18);
    plt.show();

    (df[col_name].dt.day.value_counts().sort_index()/len(df) * 100).plot(kind="bar", figsize=(15,7), grid=True);
    plt.xlabel("Day", size=14);
    plt.ylabel("Ratio (1-100)", size=14);
    plt.title(col_name + " Day Frequency Graph", size=18);
    plt.show();

def plot_catagorical_columns(cat_variable):
    if not plot_______:
        return None
    (df[cat_variable].value_counts() / len(df) * 100).plot.bar(figsize=(15,6), grid=True);
    plt.title(cat_variable, size=18, color='r');
    plt.xlabel("Catagory", size=14, color='r');
    plt.ylabel("Ratio (1-100)", size=14, color='r');
    plt.show()

def data_shape():
    return f"The Data have:\n\t{df.shape[0]} rows\n\t{df.shape[1]} columns\n"
#===
# df = pd.read_csv("data.csv", date_parser=True)

# df = pd.read_csv("df_only_selected_columns_using_PCA.csv", date_parser=True)
# target_variable = "ACTUAL_WORTH"
# df = pd.concat([
#         df.select_dtypes("number").iloc[:, :3],
#         df.select_dtypes("O").iloc[:, :3],
#         df.select_dtypes(exclude=["number", "O"]),
#         df[[target_variable]]], 1)
# target_variable = "AREA_NAME_EN"

# df = pd.read_csv("cleaned_data.csv", date_parser=True)
# target_variable = "SalePrice"

train = pd.read_csv("/home/amir/Downloads/train.csv")
test  = pd.read_csv("/home/amir/Downloads/test.csv")
target_variable = "SalePrice"
train_y = train[target_variable]
train = train.drop(columns=target_variable)
df = pd.concat([train, test])
df[target_variable] = train_y.to_list() + [None]*len(test)
#===
new_line()
print(data_shape())
#===
new_line()
print(f"Columns types distribution:\n\n{df.dtypes.value_counts()}\n")
df.dtypes.value_counts().plot(kind='barh', figsize=(10, 2), grid=True, title="Variable types Count Graph");
plt.xlabel("Count");
plt.show()
#===
f = df[target_variable].isna().sum()
if f:
    new_line()
    to_print = f"There are {f} NAs in target values, we droped those rows"
    print(colored(to_print, 'red'))
    df = df[df[target_variable].notna()]
del f
#---------------------------------------------------
# df.select_dtypes("O").columns[:5]
# D = df.select_dtypes(exclude="O")
# D2 = df.select_dtypes("O").iloc[:,:5]
# df = pd.concat([D, D2], 1)

# profile = ProfileReport(df, title='Pandas Profiling Report', explorative=True)
# profile.to_file("your_report.html")
#---------------------------------------- NA
a = df.isna().sum().where(lambda x:x>0).dropna()
if a.size:
    new_line()
    to_print = f"There are {len(a)} (out of {df.shape[1]}, [{round(len(a)/df.shape[1]*100)}%]) columns that contains 1 or more NA."
    print(colored(to_print, 'red'))

    for i in a.index:
        df[i+"_NA_indicator"] = df[i].isna().replace({True : "Missing", False : "Not missing"})
    new_line()
    to_print = f"{a.size} NA_indicator variables added to the data\n"
    print(colored(to_print, 'red'))


    print("========= NA Graphs =========\n")
    msno.matrix(df);
    plt.title("NA Graph");
    plt.show()

    new_line()
    sns.heatmap(df.isnull(), cbar=False);
    plt.title("NA Graph");
    plt.show()
#===
a = a.sort_values()/len(df)*100
if (a == 100).sum():
    new_line()
    df.drop(columns=a[a==100].index, inplace=True)
    to_print = f"There are {(a == 100).sum()} columns that are all Missing values, so we droped those.\nNow {data_shape()}\n\nDropped columns names:"
    print(colored(to_print, 'red'))
    for i in a[a==100].index:
        print("\t",i)
    a = a[a != 100]
#===
x = df[a.index].dtypes.value_counts()
if x.size:
    new_line()
    print(f"NA columns data type Distribution:\n\n{x}")
del x
#===
new_line()
if a.size:
    print(f"NaN Ratio (0-100)\n\n{a}")
else:
    print(colored("Now There is no NaN value in our Data", 'red'))
#===
# ----------------------------------------------- Imputing Missing values
# ------------------------------------ Numerical columns imputing
if df.select_dtypes("number").isna().sum().sum():
    new_line()
    print(f'(Before Missing values treatment)\nThere are {df.isna().sum().sum()} Missing values:\n\t{df.select_dtypes("O").isna().sum().sum()} in catagorical variables\n\t{df.select_dtypes("number").isna().sum().sum()} in numerical columns\n\t{df.select_dtypes(exclude=["O", "number"]).isna().sum().sum()} in others')
    from sklearn.impute import KNNImputer
    df_not_a_number  = df.select_dtypes(exclude="number")
    df_number        = df.select_dtypes("number")
    del df
    imputer = KNNImputer(n_neighbors=4, weights="uniform")
    imputed = imputer.fit_transform(df_number)
    df_number = pd.DataFrame(imputed, columns=df_number.columns)
    df = pd.concat([df_not_a_number.reset_index(drop=True), df_number.reset_index(drop=True)], axis=1)
    del df_not_a_number
    del df_number
    print(f'\n(After filling numeric missing values)\nThere are {df.isna().sum().sum()} Missing values:\n\t{df.select_dtypes("O").isna().sum().sum()} in catagorical variables\n\t{df.select_dtypes("number").isna().sum().sum()} in numerical columns\n\t{df.select_dtypes(exclude=["O", "number"]).isna().sum().sum()} in others')
#===
# -------------------------------- Catagoriacal variables imputating
vars_to_fill = df.select_dtypes("O").isna().mean().where(lambda x:x>0).dropna().sort_values(ascending=True)
if vars_to_fill.size:
    for col in vars_to_fill.index:
        tr = pd.concat([df[[col]], df.loc[:,df.isna().sum() == 0]], 1)
        tr_y = tr[col]
        tr_X = tr.drop(columns=col)

        tr_T = tr_X.select_dtypes("number")
        cat_cols = pd.get_dummies(tr_X.select_dtypes(exclude="number"), prefix_sep="__")
        tr_T[cat_cols.columns.to_list()] = cat_cols

        tr_T[col] = tr_y
        tr = tr_T.copy("deep")

        train = tr[tr[col].notna()]
        test  = tr[tr[col].isna()]

        train_y = train[col]
        train_X = train.drop(columns=col)

        test_X = test.drop(columns=col)

        clf = DecisionTreeClassifier().fit(train_X, train_y)
        test_y = clf.predict(test_X)

        df.loc[df[col].isna(), col] = test_y
    new_line()
    print(f"Missing values imputed, Now there are {df.isna().sum().sum()} Missing values")
# ----------------------------------------------- END Imputing Missing values
# --------------------------------------------------------- Unique values
only_one_unique_value = df.nunique().where(lambda x:x == 1).dropna()
if only_one_unique_value.size:
    new_line()
    df.drop(columns=only_one_unique_value.index, inplace=True)
    last_ = ("", "it") if  only_one_unique_value.size == 1 else ("s", "those")
    to_print = f"There are {only_one_unique_value.size} variable{last_[0]} That have only one unique value, so we droped {last_[1]}.\nDropped column{last_[0]} name{last_[0]} (in order):"
    print(colored(to_print, 'red'))
    for i in only_one_unique_value.index.sort_values():
        print(i)
    new_line()
    print(f"\nNow {data_shape()}")
del only_one_unique_value
# #===
all_values_are_unique = df.apply(lambda x:x.is_unique).where(lambda x:x==True).dropna()
if all_values_are_unique.size:
    new_line()
    df.drop(columns=all_values_are_unique.index, inplace=True)
    last_ = ("", "it") if  all_values_are_unique.size == 1 else ("s", "those")
    to_print = f"There are {all_values_are_unique.size} column{last_[0]} that have all unique values, so no value repeatation, we droped {last_[1]} column{last_[0]}.\nDropped column{last_[0]} name{last_[0]} are:\n"
    print(colored(to_print, 'red'))
    for i in all_values_are_unique.index:
        print("\t", i)
    new_line()
    print(f"Now {data_shape()}")
del all_values_are_unique
#===
date_columns = []
def DTYPES():
    global date_columns
    catagorical_columns = df.head().select_dtypes("O").columns
    numerical_columns   = df.head().select_dtypes("number").columns
    date_columns        = []

    for i in catagorical_columns:
        try:
            df[i] = pd.to_datetime(df[i])
            date_columns.append(i)
        except:
            pass

    catagorical_columns = catagorical_columns.drop(date_columns)
    if date_columns:
        date_columns = pd.Index(date_columns)
    #===
    if not catagorical_columns.append(numerical_columns).append(date_columns).is_unique:
        new_line()
        print(colored("Some column/s repated in > 1 dtypes\n", 'red'))
        dtypes = pd.DataFrame({"Column" : catagorical_columns.append(numerical_columns).append(date_columns),
                    "dtype" : ['O']*len(catagorical_columns) + ['Number']*len(numerical_columns) + ['Date']*len(date_columns)})
        print(dtypes[dtypes.Column.isin(list(dtypes[dtypes.Column.duplicated()].Column.values))].to_string())
    #===
    x = df.columns.difference(
        catagorical_columns.append(numerical_columns).append(date_columns)
        )
    if x.size:
        new_line()
        print(colored("Some columns not included in any existing catagory, those:\n", 'red'))
        for i in x:
            print(f"\t<{i}, with dtype of <{df[i].dtype}>")
    #===
    dtypes = pd.DataFrame({"Column" : catagorical_columns.append(numerical_columns).append(date_columns),
                "dtype" : ['Object']*len(catagorical_columns) + ['Number']*len(numerical_columns) + ['Date']*len(date_columns)})
    return dtypes
#===
dtypes = DTYPES()
# ----------------------------------------------------------------------- Feature enginearing
# ======= Adding date columns
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> add polynomial, sqrt, tree, log features
def add_new_date_cols(x, suffix):
    d = {}
    d[suffix + '_week_normalized'] = x.dt.week / 52
    d[suffix + '_week_str'] = '"' + x.dt.week.apply(lambda x:np.nan if np.isnan(x) else str(x).replace(".0", "")) + '"'

    d[suffix + '_year_after_min_year'] = x.dt.year - x.dt.year.min()
    d[suffix + '_year_str'] = '"' + x.dt.year.apply(lambda x:np.nan if np.isnan(x) else str(x).replace(".0", "")) + '"'

    d[suffix + '_day_name']  = x.dt.day_name()

    d[suffix + '_day_after_min_date_str']  = '"' + (x - x.min()).apply(lambda x: str(x).split()[0]) + '"'

    d[suffix + '_day_normalized'] = x.dt.day / 31

    d[suffix + '_hour_normalized'] = x.dt.hour / 24
    d[suffix + '_hour_str'] = '"' + x.dt.hour.apply(lambda x:np.nan if np.isnan(x) else str(x).replace(".0", "")) + '"'

    d[suffix + '_month_name'] = x.dt.month_name()
    d[suffix + '_month_normalized'] = x.dt.month/12
    for k,v in d.items():
        if v.nunique() > 1:
            df[k] = v
    return df.drop(columns=x.name)
    # return df

len_df_before_adding_date_vars = df.shape[1]
for date_col in date_columns:
    df = add_new_date_cols(df[date_col], date_col)
len_df_after_adding_date_vars  = df.shape[1]
if len_df_after_adding_date_vars > len_df_before_adding_date_vars:
    new_line()
    to_print = f"Added {len_df_after_adding_date_vars - len_df_before_adding_date_vars} date Features"
    print(colored(to_print, 'red'))

# ======= type casting of numerical variable (those who have < 4% unique values) to catagorical variables
f = (df.select_dtypes("number").nunique() / len(df) * 100).where(lambda x:x<4).dropna().index
if f.size:
    len_df_before_adding_date_vars = df.shape[1]
    for col_num_to_str in f:
        df[col_num_to_str+"_str"] = '"' + df[col_num_to_str].astype(str) + '"'
    len_df_after_adding_date_vars  = df.shape[1]
    new_line()
    to_print = f"Added {len_df_after_adding_date_vars - len_df_before_adding_date_vars} String Features (Extracted from numerical variables)"
    print(colored(to_print, 'red'))
# =======
def cluping_rare_cases_in_one_catagory(x):
    global df
    x = df[x]
    orignal  = x.copy("deep")
    xx = x.value_counts()
    xx = xx[xx< 10].index.to_list()
    x =  x.replace(xx , "Rare cases")
    if x.value_counts()[-1] < 8:
        x[x == "Rare cases"] = x.mode()[0] # agar "Rare cases" vali catogery me 8 sy bhi kam values hon to un ko most common value sy replace kar do
    if x.nunique() == 1:
        new_line()
        to_print = f"The column <{x.name}> have only one unique value, We droped it from the data."
        print(colored(to_print, 'red'))
        # return orignal
        df.drop(columns=x.name, inplace=True)
        return None
    return x

for var in df.select_dtypes("O").columns:
    m = cluping_rare_cases_in_one_catagory(var)
    if isinstance(m, pd.core.series.Series):
        df[var] = m
new_line()


xx = (df == 'Rare cases').sum().sort_values().where(lambda x:x>0).dropna()
xx = pd.DataFrame({"Count" : xx,
                "Ratio" : round(xx/len(df)*100, 4)})
print(f"<Rare case> catagory:\n{xx.to_string()}")
# ----------------------------------------------------------------------- END (Feature enginearing)
dtypes = DTYPES()
# ---------------------------------------------------- Correlation plot
new_line()
cor_df = df.select_dtypes('number').corr().abs()
mask = np.triu(np.ones_like(cor_df, dtype=bool));
f, ax = plt.subplots(figsize=(17, 10));
cmap = sns.color_palette("viridis", as_cmap=True);
plot_ = sns.heatmap(cor_df, mask=mask, cmap=cmap, vmax=.3, square=True, linewidths=.5, cbar_kws={"shrink": .5});
plot_.axes.set_title("abs (Correlation) plot",fontsize=25);
plt.show()
# ---------------------------------------------------------------------
#===
# m = 0
for row in dtypes.iterrows():
    # m += 1
    # if m == 3:
        # break
    column_name, type_ = row[1]
    x = df[column_name]
    to_print = "f\n\n\n========================================= {column_name} =========================================\n\n"
    print(colored(to_print, 'red'))

    for col_ in df.columns:
        if col_ == column_name:
            continue
        if df[col_].nunique() == df[column_name].nunique():
            unique_combination = df[[col_, column_name]].drop_duplicates()
            if unique_combination.apply(lambda x:x.is_unique).sum() == 2:
                new_line()
                to_print = f"This Columns is duplicate of <{col_}> column"
                print(colored(to_print, 'red'))

    # print(f"Column Type     : {type_}")
    print(f"Column Type     : ", end="")
    print(colored(type_, 'red'))
    if x.isna().all():
        new_line()
        df.drop(columns=column_name, inplace=True)
        print(colored("We dropped This column, because it is all Empty", 'red'))
        continue
    if type_ in ["O", "Date"]:
        if x.is_unique:
            new_line()
            df.drop(columns=column_name, inplace=True)
            to_print = f"We dropped This column, because it's a {type_} columns, and it's all values are unique"
            print(colored(to_print, 'red'))
            continue
    if x.nunique() == 1:
        new_line()
        df.drop(columns=column_name, inplace=True)
        print(colored("We dropped This column, because There is only one unique value", 'red'))
        continue

    if type_ == "Number":
        local_cor = cor_df[column_name].drop(column_name).reset_index()
        local_cor = local_cor.reindex(local_cor[column_name].abs().sort_values().index)
        if local_cor[column_name].max() == 1:
            new_line()
            to_print = f"This column is perfactly correlated with column <{local_cor[local_cor[column_name] == 1]['index'].values[0]}, so remove one of them"
            print(colored(to_print, 'red'))

        new_line()
        xm = local_cor[-3:].rename(columns={'index' : 'Column name', column_name : 'Correlation'}).reset_index(drop=True)
        xm.index = xm['Column name']
        xm.drop(columns="Column name", inplace=True);
        xm.plot(kind='barh', grid=True, figsize=(10,1.5));
        plt.title("Most 3 correlated features with this columns (sorted)", size=14);
        plt.xlabel("Correlation", size=12);
        plt.show();

        new_line()
        skewness = x.skew(skipna = True)
        if abs(skewness) < 0.5:
            print(f"The data is fairly symmetrical (skewness is: {skewness})")
        elif abs(skewness) < 1:
            print(f"The data are moderately skewed (skewness is: {skewness})")
        else:
            to_print = f"The data are highly skewed (skewness is: {skewness})\nNote: When skewness exceed |1| we called it highly skewed"
            print(colored(to_print, 'red'))

        # f = x.describe()
        # f['Nunique'] = x.nunique()
        # f['Nunique ratio'] = f.loc["Nunique"] / f.loc["count"] * 100
        # f['Outlies count'] = (((x - x.mean())/x.std()).abs() > 3).sum()
        # f['Outlies ratio'] = f.loc["Outlies count"] / f.loc["count"] * 100
        # f['Nagative values count'] = (x < 0).sum()
        # f['Nagative values ratio'] = f['Nagative values count'] / f['count'] * 100

        ff = [x.count(), x.isna().sum(), x.mean(), x.std(), x.min()]
        ff += x.quantile([.25,.5,.75]).to_list()
        ff += [x.max(), x.nunique(), (((x - x.mean())/x.std()).abs() > 3).sum(), (x < 0).sum(), (x == 0).sum()]

        f = pd.DataFrame(ff, index=['Count', 'NA', 'Mean', 'Std', 'Min', '25%', '50%', '75%', 'Max', 'Nunique', 'Outlies', 'Nagetive', 'Zeros'], columns=['Count'])
        f['Ratio'] = f.Count / x.count() * 100
        f.loc['Mean' : 'Max', 'Ratio'] = None

        new_line()
        print(f.round(2).to_string())
        plot_numerical_columns(column_name)

    elif type_ == "Object":
        # f = x.describe()
        # f = x.agg(['count', pd.Series.nunique])
        # f['len'] = len(x)
        # f['Na count'] = x.isna().sum()
        # f['Na ratio'] = f['Na count'] / f['count'] * 100
        # f['Most frequent'] = x.mode().values[0]
        # f['Most frequent count'] = (x == f['Most frequent']).sum()
        # f['Most frequent ratio'] = f['Most frequent count'] / f['count'] * 100
        # f['Least frequent'] = x.value_counts().tail(1).index[0]
        # f['Least frequent count'] = (x == f['Least frequent']).sum()
        # f['Least frequent ratio'] = f['Least frequent count'] / f['count'] * 100
        # f['Values occured only once count'] = x.value_counts().where(lambda x:x==1).dropna().size
        # f['Values occured only once Ratio'] = f['Values occured only once count'] / x.count() * 100

        l = x.count(), x.nunique(), len(x), x.isna().sum(), (x == x.mode().values[0]).sum(), (x == x.value_counts().tail(1).index[0]).sum(), x.value_counts().where(lambda x:x==1).dropna().size
        f = pd.DataFrame(l, index=['Count', 'Nunique', 'Len', 'NA', 'Most frequent', 'Least frequent', 'Values occured only once'], columns=['Counts'])
        f['Ratio'] = (f.Counts / x.count() * 100).round(4)
        f.loc[['Len'], 'Ratio'] = None

        new_line()
        print(f.to_string())


        if x.str.lower().nunique() != x.nunique():
            new_line()
            to_print = f"Case issue\n\tin orignal variable There are {x.nunique()} unique values\n\tin lower verstion there are   {x.str.lower().nunique()} unique values.\n"
            print(colored(to_print, 'red'))

        if x.str.strip().nunique() != x.nunique():
            new_line()
            to_print = f"Space issue\n\tin orignal variable There are {x.nunique()} unique values\n\tin striped verstion there are {x.str.strip().nunique()} unique values."
            print(colored(to_print, 'red'))

        plot_catagorical_columns(column_name)

    elif type == "Date":

        new_line()
        rd = relativedelta.relativedelta( pd.to_datetime(x.max()), pd.to_datetime(x.min()))
        to_print = f"Diffrenece between first and last date:\n\tYears : {rd.years}\n\tMonths: {rd.months}\n\tDays  : {rd.days}"
        print(colored(to_print, 'red'))

        # f = pd.Series({'Count' : x.count(),
        #             'Nunique count' : x.nunique(),
        #             'Nunique ratio' : x.nunique() / x.count() * 100,
        #             'Most frequent value' : str(x.mode()[0]),
        #             'Least frequent value' :  x.value_counts().tail(1).index[0]
        #             })
        # f['Most frequent count'] = (x == f['Most frequent value']).sum()
        # f['Most frequent ratio'] = f['Most frequent count'] / f['Count'] * 100
        # f['Least frequent count'] = (x == f['Least frequent value']).sum()
        # f['Least frequent ratio'] = f['Least frequent count'] / f['Count'] * 100
        # f['Values occured only once count'] = x.value_counts().where(lambda x:x==1).dropna().size
        # f['Values occured only once Ratio'] = f['Values occured only once count'] / x.count() * 100

        ff = x.count(), x.nunique(), (x == x.mode().values[0]).sum(), (x == x.value_counts().tail(1).index[0]).sum(), x.value_counts().where(lambda x:x==1).dropna().size
        f = pd.DataFrame(ff, index=["Count", 'Nunique', 'Most frequent values', 'Least frequent values', 'Values occured only once count'], columns=['Counts'])
        f['Ratio'] = (f.Counts / x.count() * 100).round(4)

        new_line()
        print(f"\n{f.to_string()}")


        f = set(np.arange(x.dt.year.min(),x.dt.year.max()+1)).difference(
            x.dt.year.unique())
        if f:
            new_line()
            print(colored("These Years (in order) are missing:\n", 'red'))
            for i in f:
                print("\t", i, end=", ")

        f = set(np.arange(x.dt.month.min(),x.dt.month.max()+1)).difference(
            x.dt.month.unique())
        if f:
            new_line()
            print(colored("These Months (in order) are missing:\n", 'red'))
            for i in f:
                print("\t", i, end=", ")

        f = set(np.arange(x.dt.day.min(),x.dt.day.max()+1)).difference(
            x.dt.day.unique())
        if f:
            new_line()
            print(colored("These Days (in order) are missing:\n", 'red'))
            for i in f:
                print("\t", i, end=", ")

        new_line()
        plot_date_columns(column_name)


# ================================================================================================================ Modeling
print("\n\n")
print("----------------------------------------------------------------------------------------------")
print("****************************************** Modeling ******************************************")

# Regression problem
if df[target_variable].dtype in [float, int]:

    print("\n-------------------- This is Regression problem --------------------\n")
    print("''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''")

    df_T = df.select_dtypes("number")
    cat_cols = pd.get_dummies(df.select_dtypes(exclude="number"), prefix_sep="__")
    df_T[cat_cols.columns.to_list()] = cat_cols

    df = df_T.copy("deep")
    del df_T
    del cat_cols
    # ====
    train_X, test_X, train_y, test_y = train_test_split(df.drop(columns=target_variable), df[target_variable])
    # ====
    # --------------------------------------------------------- Linear regression
    to_print = "\n ------------------------------------- Linear Regression -------------------------------------\n"
    print(colored(to_print, 'red'))

    model_reg = OLS(train_y, train_X).fit()
    summary = model_reg.summary()
    summary_df = pd.DataFrame(summary.tables[1])
    summary_df.columns = summary_df.iloc[0]
    summary_df.drop(0, inplace=True)
    summary_df.columns = summary_df.columns.astype(str)
    summary_df.columns = ["Variable"] + summary_df.columns[1:].to_list()
    for i in summary_df.columns[1:]:
        summary_df[i] = summary_df[i].astype(str).astype(float)
    summary_df.Variable = summary_df.Variable.astype(str)
    summary_df['Indicator'] = summary_df['P>|t|'].apply(lambda x:"***" if x < 0.001 else "**" if x < 0.01 else "*" if x < 0.05 else "." if x < 0.1  else "")
    summary_df = summary_df.sort_values("Variable").reset_index(drop=True)
    summary_df.to_csv()
    new_line()
    print(colored("NOTE: This summary saved as <summary_OLS_1.csv>", 'red'))

    new_line()
    print(summary_df.to_string())
    # ============================= Model statistic
    predictions = model_reg.predict(test_X)

    new_line()
    print(colored(" --- Model statistic --- \n", 'red'))
    print(f"R-squared         : {round(model_reg.rsquared, 3)}")
    print(f"Adj. R-squared    : {round(model_reg.rsquared_adj, 3)}")
    print(f"F-statistic       : {round(model_reg.fvalue)}")
    print(f"Prob (F-statistic): {model_reg.f_pvalue}")
    print(f"No. Observations  : {round(model_reg.nobs)}")
    print(f"AIC               : {round(model_reg.aic)}")
    print(f"Df Residuals      : {round(model_reg.df_resid)}")
    print(f"BIC               : {round(model_reg.bic)}")
    print(f"RMSE (test)       : {RMSE(predictions)}")
    # ======
    f = train_X.copy("deep")
    f['Errors__'] = model_reg.resid
    f = f.corr()['Errors__'].drop("Errors__").abs().sort_values().dropna().tail(1)
    new_line()
    print(f"Maximum correlation between Reseduals and any data columns is {f.values[0]}, with columns <{f.index[0]}>")
    print(f"Mean of train reseduals: {model_reg.resid.mean()}")
    del f
    # ============================= END (Model statistic)
    # --------------------------------------------------------- END Linear regression




    # --------------------------------------------------------- Random Forest
    print("\n ------------------------------------- Random Forest -------------------------------------\n")

    rf = RandomForestRegressor(n_estimators = 200, oob_score=True)
    model_rf = rf.fit(train_X, train_y);
    predictions_rf = rf.predict(test_X)

    new_line()
    print(colored("RF model peramters:\n", 'red'))
    pprint.pprint(model_rf.get_params())

    new_line()
    importances = list(rf.feature_importances_)
    feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(test_X, importances)]
    featuresImportance = pd.Series(model_rf.feature_importances_, index=train_X.columns).sort_values(ascending=False)
    if len(featuresImportance) > 30:
        featuresImportance = featuresImportance.head(30)
    featuresImportance.plot(figsize=(20,10), kind='bar', grid=True);
    plt.title("RandomForest Feature importances Graph", size=18,color='red');
    plt.xlabel("Features", size=14, color='red');
    plt.ylabel("Importance", size=14, color='red');
    plt.show();
    del featuresImportance

    new_line()
    print(colored("--- Model statistic ---", 'red'))
    # The coefficient of determination R^2 of the prediction.
    # https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
    print(f"R^2 (test) : {rf.score(test_X, test_y)}")
    print(f"R^2 (train): {rf.score(train_X, train_y)}")
    print(f"RMSE (test): {RMSE(predictions_rf)}")
    print(f"oob score  : {model_rf.oob_score_}")

    f = test_X.copy("deep")
    errors_rf = predictions_rf - test_y
    f['Errors__'] = errors_rf
    f = f.corr()['Errors__'].drop("Errors__").abs().sort_values().dropna().tail(1)
    new_line()
    print(f"Maximum correlation between Reseduals and any data columns is {f.values[0]}, with columns <{f.index[0]}>")
    # --------------------------------------------------------- END Random Forest
elif df[target_variable].dtype == "O":
    # Classififcation problem
    if df[target_variable].nunique() == 2:
        print("\n-------------------- This is Binary Classification problem --------------------\n")
        print("''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''")
        df = pd.concat([
                        df.select_dtypes(exclude = "O"),
                        pd.get_dummies(df.drop(columns=target_variable).select_dtypes("O")),
                        df[[target_variable]]
                        ], 1)

        train_X, test_X, train_y, test_y = train_test_split(df.drop(columns=target_variable), df[target_variable])
        clf = LogisticRegression().fit(train_X, train_y)
        predictions = clf.predict_proba(test_X)
        predictions = pd.Series(predictions[:, 0])
        lst = []
        for thresh in np.linspace(predictions.min(), predictions.max(), 50)[1:]:
            pred = predictions < thresh

            pred.loc[pred == True] = clf.classes_[0]
            pred.loc[pred == False] = clf.classes_[1]

            test_y = test_y.reset_index(drop=True)

            TN = ((pred == clf.classes_[0]) & (test_y == clf.classes_[0])).sum()
            TP = ((pred == clf.classes_[1]) & (test_y == clf.classes_[1])).sum()
            FN = ((pred == clf.classes_[0]) & (test_y == clf.classes_[1])).sum()
            FP = ((pred == clf.classes_[1]) & (test_y == clf.classes_[0])).sum()

            p = TP / (TP + FP)
            r = TP / (TP + FN)
            f = 2 * ((p * r) / (p+r))

            lst.append((thresh, (pred == test_y).mean(), p, r , f))

        d = pd.DataFrame(lst, columns=["Thresold", "Accurecy(0-1)", "Precision", "Recall", "F1"])
        d = d.set_index("Thresold")
        d.plot(grid=True, figsize=(18,7));
        plt.title("Model performance at diffrent Thresolds", size=18, color='red');
        plt.xlabel("Thresold", size=14, color='red');
        plt.ylabel("");
        plt.show()
    else:
        to_print = "\n-------------------- This is Multiclass Classification problem --------------------\n"
        print(colored(to_print, 'red'))
        print("'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''")

        df.loc[:, df.select_dtypes("O").columns] = df.select_dtypes("O").apply(lambda x: pd.Series(LabelEncoder().fit_transform(x.astype(str))).astype(str))
        train_X, test_X, train_y, test_y = train_test_split(df.drop(columns=target_variable), df[target_variable])

        clf=RandomForestClassifier(n_estimators=1000).fit(train_X, train_y)
        predictions = clf.predict(test_X)
        feature_imp = pd.Series(clf.feature_importances_,index=train_X.columns).sort_values(ascending=False)
        if feature_imp.size > 30:
            feature_imp = feature_imp.head(30)
        feature_imp.plot(kind='barh', figsize=(17,10), grid=True);
        plt.title("Feature importances Graph", size=18, color='red');
        plt.xlabel("Importance", size=14, color='red');
        plt.ylabel("Feature", size=14, color='red');
        plt.show()
        # ====
        f = (test_y, predictions)
        f_int = (test_y.astype(int), predictions.astype(int))

        print(f"accuracy_score: {metrics.accuracy_score(*f)}")
        print(f"f1_score: {metrics.f1_score(*f_int)}")

        metrics.plot_roc_curve(clf, test_X, test_y);
        plt.title("ROC curve plot");
        plt.show();

        metrics.ConfusionMatrixDisplay(metrics.confusion_matrix(*f)); plt.show()

        metrics.plot_confusion_matrix(clf, test_X, test_y);
        plt.title("Confusion matrix");
        plt.show()

        metrics.plot_precision_recall_curve(clf, test_X, test_y);
        plt.title("Precision recall curve");
        plt.show()
# ================================================================================================================ END Modeling

-------------------------

The Data have:
	2919 rows
	81 columns


-------------------------

Columns types distribution:

object     43
int64      26
float64    12
dtype: int64

-------------------------

There are 1459 NAs in target values, we droped those rows

-------------------------

There are 19 (out of 81, [23%]) columns that contains 1 or more NA.

-------------------------

19 NA_indicator variables added to the data

========= NA Graphs =========

-------------------------

-------------------------

NA columns data type Distribution:

object     16
float64     3
dtype: int64

-------------------------

NaN Ratio (0-100)

Electrical       0.068493
MasVnrType       0.547945
MasVnrArea       0.547945
BsmtQual         2.534247
BsmtCond         2.534247
BsmtFinType1     2.534247
BsmtExposure     2.602740
BsmtFinType2     2.602740
GarageCond       5.547945
GarageQual       5.547945
GarageFinish     5.547945
GarageType       5.547945
GarageYrBlt      5.547945
LotFrontage     17.739726
FireplaceQu     47.260274
Fence           80.753425
Alley           93.767123
MiscFeature     96.301370
PoolQC          99.520548
dtype: float64

-------------------------

(Before Missing values treatment)
There are 6965 Missing values:
	6617 in catagorical variables
	348 in numerical columns
	0.0 in others

(After filling numeric missing values)
There are 6617 Missing values:
	6617 in catagorical variables
	0 in numerical columns
	0.0 in others

-------------------------

Missing values imputed, Now there are 0 Missing values

-------------------------

There are 1 column that have all unique values, so no value repeatation, we droped it column.
Dropped column name are:

	 Id

-------------------------

Now The Data have:
	1460 rows
	99 columns


-------------------------

Added 18 String Features (Extracted from numerical variables)

-------------------------

The column <Street> have only one unique value, We droped it from the data.

-------------------------

The column <Utilities> have only one unique value, We droped it from the data.

-------------------------

The column <Electrical_NA_indicator> have only one unique value, We droped it from the data.

-------------------------

The column <PoolQC_NA_indicator> have only one unique value, We droped it from the data.

-------------------------

The column <PoolArea_str> have only one unique value, We droped it from the data.

-------------------------

<Rare case> catagory:
                         Count   Ratio
HouseStyle                 8.0  0.5479
MasVnrType_NA_indicator    8.0  0.5479
MasVnrArea_NA_indicator    8.0  0.5479
FullBath_str               9.0  0.6164
Foundation                 9.0  0.6164
RoofStyle                  9.0  0.6164
MiscFeature                9.0  0.6164
Neighborhood              11.0  0.7534
Heating                   14.0  0.9589
BedroomAbvGr_str          14.0  0.9589
Condition1                15.0  1.0274
Condition2                15.0  1.0274
RoofMatl                  15.0  1.0274
Exterior2nd               17.0  1.1644
3SsnPorch_str             24.0  1.6438
LowQualFinSF_str          26.0  1.7808
SaleType                  28.0  1.9178
MiscVal_str               41.0  2.8082

-------------------------

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        5    0.3425
Len                         1460       NaN
NA                             0    0.0000
Most frequent               1151   78.8356
Least frequent                10    0.6849
Values occured only once       0    0.0000

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        2    0.1370
Len                         1460       NaN
NA                             0    0.0000
Most frequent                829   56.7808
Least frequent               631   43.2192
Values occured only once       0    0.0000

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        4    0.2740
Len                         1460       NaN
NA                             0    0.0000
Most frequent                925   63.3562
Least frequent                10    0.6849
Values occured only once       0    0.0000

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        4    0.2740
Len                         1460       NaN
NA                             0    0.0000
Most frequent               1311   89.7945
Least frequent                36    2.4658
Values occured only once       0    0.0000

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        4    0.2740
Len                         1460       NaN
NA                             0    0.0000
Most frequent               1056   72.3288
Least frequent                47    3.2192
Values occured only once       0    0.0000

f


========================================= {column_name} =========================================


Column Type     : Object

-------------------------

                          Counts     Ratio
Count                       1460  100.0000
Nunique                        3    0.2055
Len                         1460       NaN
NA                             0    0.0000
Most frequent               1382   94.6575
Least frequent                13    0.8904
Values occured only once       0    0.0000